System and Methods of Prediction of Ischemic Brain Tissue Fate from Multi-Phase CT-Angiography in Patients with Acute Ischemic Stroke using Machine Learning

ABSTRACT

The invention relates to systems and methods for predicting ischemic brain tissue fate from multi-phase CT-angiography. More specifically, systems and methods are provided that enable meaningful prediction of core, penumbra and perfusion from mCTA images using software that has been trained via machine learning to interpret mCTA images.

TECHNICAL FIELD

The invention relates to systems and methods for predicting ischemicbrain tissue fate from multi-phase CT-angiography (mCTA). Morespecifically, systems and methods are described that enable meaningfulprediction of core, penumbra and perfusion from mCTA images usingsoftware that has been trained via machine learning to interpret mCTAimages.

BACKGROUND

Ischemic stroke is an acute disease where tissue death (infarction)within the brain of different patients will progress at different ratesfrom the time of the ischemic event. The rate of infarction within apatient depends on a large number of physiological factors.

For the physician diagnosing and treating ischemic strokes, when astroke patient arrives at a hospital, it is very important for thephysician to obtain as much knowledge about the nature of the stroke assoon as possible in order to make an effective diagnosis and effectivedecisions regarding treatment. As is readily understood, time to effectdiagnosis and treatment is very important as faster diagnoses willimpact treatment decisions and can minimize the amount of brain tissuethat is ultimately affected as a result of the stroke.

For example, in the case of an ischemic stroke, it is important for thephysician to know where the vessel occlusion is, how big the occlusionis, where any unsalvageable brain tissue (“core”) is and, how big andwhere is the brain tissue that may have been affected by the ischemicevent but that may potentially be saved (“penumbra”).

More specifically, the penumbra is tissue around the ischemic event thatcan potentially stay alive for a number of hours after the event due toperfusion of this tissue by collateral arteries that may be providingsufficient blood and oxygen to prevent this tissue from dying for aperiod of time.

When the physician has good information about the collaterals and howthe collaterals may be located in and around the penumbra, treatmentdecisions can be made that can significantly affect patient outcomes.

In an emergency or acute situation, the process of making a decisionwill consider the amount of information at a given moment in time. Thatis, a definitive ‘yes’ decision can be made to take action or a ‘no’decision can be made to take no action based on the current information.In addition, a third decision choice can be to wait for additionalinformation. In the situation of acute stroke (and other emergencyscenarios), time to make a definitive diagnostic/treatment decision mustbe balanced against the likelihood of a negative outcome that resultssimply from the delay in making a decision. In other words, the decisionto wait for more information must consider what the effects of a delayin making a decision might be.

At the present time, in many treatment centers, when a stroke patientarrives, the assessment protocol is generally as follows:

-   a. Conduct a CT scan of the head to rule out or look for evidence of    a hemorrhagic stroke.-   b. Conduct a CT angiogram (CTA) to locate the site of vessel    occlusion.-   c. Conduct a CT perfusion (CTP) study to create perfusion maps that    provide the physician with information about various parameters    including cerebral blood flow, cerebral blood volume and mean    transit time.

An alternative to a CTP perfusion study is to conduct a “multi-phase” CTangiogram (mCTA) study. An mCTA study differs from a CTP study in thatsignificantly fewer images are taken compared to a CTP study butsufficient to make legitimate diagnosis/treatment decisions. As such,mCTA studies can be advantaged over CTP studies as they can beundertaken more rapidly with less radiation exposure to the patient.

As is known, each of these generalized steps will be affected by a largenumber of factors and the time to complete each of them will be variablefrom patient to patient and between different treatment centers. Forexample, such factors may include resource availability (e.g., trainedmedical staff and equipment) as well as processing times required by CTscan equipment and other ancillary hardware and software to present datato physicians.

For the purposes of illustration, these factors are described in termsof a representative diagnosis and treatment scenario of a patientexhibiting symptoms of a stroke, the patient arriving at the emergencyroom of a treatment center and who thereafter receives the above CTprocedures as part of the diagnostic protocol. Table 1 summarizes anumber of the key process steps and typical times that may be requiredto complete each step and are discussed below.

TABLE 1 Typical Diagnostic Steps and Completion Times Procedure Time(minutes) Elapsed Total Comments Initial Assessment 3-5 3-5 Transfer andPreparation for CT Scan 20 23-25 CT Scan 1 24-26 CT Scan Interpretationand CT Angiogram Preparation 2-3 26-30 CT Angiogram Preparation may beconcurrent with CT Scan Interpretation CT Angiogram Procedure 1-3 27-33CT Angiogram Post Processing 2 29-35 CT Angiogram Interpretation and CTPerfusion Preparation 4 (minimum) 33-39 CT Perfusion Preparation may beconcurrent with CT Scan Interpretation CT Perfusion Procedure 1 34-40 CTPerfusion Post Processing Variable 2-10 (minimum) 36-50 Will depend onvendor specifics CT Perfusion Interpretation Variable 2-10 (minimum)38-60 Will depend factors including: time of day; center; vendorequipment etc.

Upon arrival at the treatment center, an emergency room physicianconducts a preliminary assessment of the patient. If the preliminaryassessment concludes a potential stroke, the patient is prepared for aCT scan. The time taken to initially assess a potential stroke patientupon arrival at the treatment facility may be 3-5 minutes.

Preparing the patient for a CT scan involves a number of steps includingtransferring the patient to the CT imaging suite and connecting anintra-venous line to the patient to enable the injection of contrastagent into the patient during the various CT procedures.

The CT scan includes conducting an x-ray scan of the patient togetherwith a computerized analysis of the x-ray data collected. Morespecifically, as is known, during a CT scan, beams of x-rays are emittedfrom a rotating device through the area of interest in the patient’sbody from several different angles to receivers located on the oppositesides of the body. The received data is used to create projectionimages, which are then assembled by computer into a two or athree-dimensional picture of the area being studied. More specifically,the computer receives the x-ray information and uses it to createmultiple individual images or slices which are displayed to thephysician for examination.

CT scans require that the patient hold still during the scan becausesignificant movement of the patient will cause blurred images. This issometimes difficult in stroke patients and hence sometimes headrestraints are used to help the patient hold still. Complete scans takeonly a few minutes.

Upon completion of the initial CT scan including the post-processingtime to assemble the images, the physician interprets the images todetermine a) if a stroke has occurred and, b) if so, to determine if thestroke is hemorrhagic or ischemic. If the stroke is hemorrhagic,different procedures may be followed. It will typically take thephysician in the order of 1-2 minutes from the time the images areavailable to make the determination that the stroke is hemorrhagic orischemic.

If the stroke is ischemic, the decision may be made to conduct a CTangiogram (CTA).

CT angiography procedures generally require that contrast agents beintroduced into the body before the scan is started. Contrast is used tohighlight specific areas inside the body, in this case the bloodvessels. In addition, because of presence of contrast in the very smallvessels of the brain, overall, the brain looks brighter (has a higherHounsfield value) also known as contrast enhancement. Contrast agentsare iodine-based compounds that inhibit the passage of x-rays throughthe tissue. As such, they can be effective in enhancing the distinctionbetween tissues where the contrast agent is present compared to thosetissues where it is not. The CT angiogram requires additionalpreparation time but will typically not require that the patient bemoved. Generally, CT angiogram procedures involve the injection of abolus of contrast through an IV line followed by the CT scan. A typicalcontrast bolus may be 70-100 ml injected at 5 ml/second. The volume andinjection rate of contrast is determined by the procedure being followedand is generally injected in a minimally sufficient volume to be presentin the tissues of interest at the time the CT scan is conducted. Over arelatively short time period, the contrast becomes diffused within thebody thereby providing only a relatively short window of time to conducta CT procedure.

The CT angiogram data is substantially greater than what is collectedfrom a basic scan and like a basic CT scan must be subjected topost-processing to create the images. The post-processing time istypically in the range of 2-5 minutes.

After processing, the physician interprets the data and makes a decisionregarding treatment. Generally, the physician is looking to determine a)where is the occlusion? b) what is the size of the core? and c) obtain aqualitative feel for penumbra and collaterals.

Ultimately, and based on these factors, the physician is looking to makea decision on what brain tissue is worth fighting for. In other words,based on the combination of all these factors, the physician is lookingto decide either that very little, or no penumbra can be saved, oralternatively that it appears that penumbra can be saved and it isworthwhile to do so.

The CT angiogram provides relatively little data about collaterals andperfusion to the ischemic tissue as it is only a picture of the brain atone instance in time. That is, as it takes time for contrast agent toflow through the brain tissues and such flow will be very dependent onthe ability of vessels to carry the contrast agent, a single snapshot intime does not give the physician enough information to make a diagnosticand/or treatment decision. Hence, CT perfusion (CTP) procedures/studiesmay be undertaken to give the physician a more qualitative andquantitative sense of brain perfusion. Like CT angiogram, CT perfusionprocedures involve the injection of contrast agent into the patient. Itshould also be noted that some centers may choose to do a CT perfusionstudy before the CT angiogram because they feel that the contrastinjection from the CT angiogram interferes with the quality of data ofthe CT perfusion.

Perfusion computed tomography (CTP) allows qualitative and quantitativeevaluation of cerebral perfusion by generating maps of variousparameters including cerebral blood flow (CBF), cerebral blood volume(CBV), and mean transit time (MTT). The technique is based on thecentral volume principle (CBF = CBV/MTT) and requires the use of complexsoftware employing complex deconvolution algorithms to produce perfusionmaps. Other maps such as Tmax maps may also be created.

CTP studies are acquired with repeated imaging through the brain whilethe contrast is injected. The technique varies significantly from vendorto vendor and also from center to center, relies on certainphysiological assumptions that are not always valid and hence requiresspecialized training with the specific equipment at each center. CTPtypically involves imaging of the brain over approximately 60-70 seconds(at 1-4 second intervals) in order to acquire multiple images. Thetechnique is quite vulnerable to patient motion and also requires thepatient to hold still for the period. Furthermore, CTP also involvessubstantial radiation exposure in the range of 3.5-5 mSv as the numberof images taken over the time period is significant.

The procedure generates a large dataset that must then be transferred toa dedicated workstation for post-processing. This step may take over 3-5minutes in order to produce separate maps of each of CBF, CBV, and MTT.The perfusion maps are typically color-coded maps.

Importantly, the post-processing requires the use of specialized andvery often proprietary software that must be run by trained individuals.Ultimately, the time taken to fully complete CTP acquisition andanalysis is highly variable as the above factors including the vendor,the speed of data transfer, local expertise, the time of day the studyis being undertaken (i.e., working hours vs. after hours) as well asother factors can all have an effect on the actual amount of timerequired to complete the study.

Thus, while perfusion CT is not a perfect technique, it has been foundto be useful for noninvasive diagnosis of cerebral ischemia andinfarction as it does provide some degree of quantitative determinationof core and penumbra. However, as noted above, there are problems withthese procedures. In summary, these problems include:

-   a. CT perfusion takes time to complete (8-30 \+ minutes total).-   b. Patient motion can affect results.-   c. Significant post-processing time is required to complete a full    perfusion map.-   d. Additional radiation exposure to the patient.-   e. Need for additional contrast agents.-   f. Non-standardized procedures for completing the perfusion map.-   g. Variations in technique with different vendor equipment.-   h. Physiological assumptions that are not always valid.-   i. Lack of consensus in the medical community regarding the    interpretation and best practices for treatments based on the CT    perfusion maps.-   j. Lack of information regarding rate of infarct growth.-   k. Significant variability across vendors for the degree of coverage    of the brain (e.g., 4 to 16 cms). Also, some vendors have the option    of covering 8 cm using a ‘toggle table’ technique that may introduce    additional errors.

Multi-phase CTA (mCTA) has proven to be an effective alternative to CTPas a means of providing faster and usable information to enable aphysician to make effective diagnosis and treatment decisions whilesubjecting the patient to lower amounts of radiation. However, undervarious imaging scenarios, there has been a need for additionalinformation in addition to the mCTA images to improve the precision ofdiagnoses and ideally to improve the presentation of information to aphysician and specifically enable the utilization of mCTA images toprovide effective core, penumbra and perfusion maps from mCTA images.

Medium Vessel Occlusion

Medium vessel occlusion (MeVO) as compared to large vessel occlusion(LVO) and small vessel occlusion (SVO) is generally defined as occlusionof vessels distal to level 1 brain vessels and generally refers toocclusions within level 2 (approximate 2 mm diameter) and level 3vessels (approximate 1 mm diameter). As is known, level 1 and level 2vessels are generally referred by the relative location of these vesselswith respect to a frontal plane including anterior (A), posterior (P)and middle (M) positions. Thus, for reference, A2 and A3 vessels areanterior level 2 and 3 vessels, M2 and M3 vessels are middle level 2 and3 vessels and P2 and P3 vessels are posterior level 2 and 3 vessels (seeFIG. 7 ).

As is known, the anatomy of brain vessels is such that with eachbifurcation, the relative size of daughter vessels becomes smaller, andthe volume of tissue perfused downstream of each bifurcation alsobecomes smaller. In addition, with each bifurcation to smaller vessels,the variability in anatomy between people becomes higher, the tortuosityof vessels becomes higher, and the total number of junctions anddefinable zones becomes higher within a larger region.

As a result, the ability to determine the location of MeVO becomes moredifficult as the number of zones/areas where the MeVO may besubstantially higher.

At present, MeVO (as compared to LVO or SVO) is diagnosed by thephysician by carefully looking at the source images of the CT angiogram.Looking at the CTP maps can be of help. That is, if a zone of the brainis observed as having an affected area (penumbra and core) at aparticular level(s) or zone as shown by the CTA images and/or CTP map,the physician will look to areas/zones proximal to that area/zone todetermine which vessel may be occluded and is causing the affectedtissue. In order to locate the occlusion, the problem is more difficultthan with LVOs for the reasons outlined above and specifically becausethe number of potential zones is larger (with each zone also beingsmaller), the vessels are smaller, the anatomy is more variable, and thetortuosity of vessels may be greater. As such, the physician, based ontheir knowledge of brain anatomy will look for the specific vessel byexamining raw contrast CTA images for particular zones proximal to theaffected tissue that show evidence of contrast either being held up orhaving cleared. Factors including the location, size/volume, shape,confluence, involvement of the cortex and sub-cortical white matter, andknowledge of the known supply by vessels may be taken into considerationin determining whether an occlusion is an LVO, MeVO or SVO.

For example, it may be observed that a left frontal region of aparticular size and shape just cranial to the Sylvian fissure isischemic. It is then expected that that region is supplied by one of thebranches of the anterior division of the MCA (anterior M2 or one of itsbranches). From this knowledge, the physician will look at imagesproximal to the hypoperfused region to locate and observe the vessels todetermine where an occlusion may be. By observing the behavior of thecontrast across different phases of images, the physician may observethat of 4 vessels in a zone, vessels 1, 2 and 4 are open whereas vessel3 is occluded. Thus, from manually observing these vessels the site ofthe occlusion can be determined. Based on the size and location of theischemic tissue, LVO is excluded.

This process can be quite time consuming and requires a high level ofexpertise that may not be available 24/7 at many centres and requiretime being spent moving backwards and forwards through images to trace anumber of specific vessels to hunt for the single vessel that isoccluded.

Accordingly, there has been a need for improved systems able to assistin the diagnosis of MeVO.

SUMMARY

In accordance with the invention, systems, and methods for predictingischemic brain tissue fate from multi-phase CT-angiography (mCTA). Morespecifically, systems and methods are described that enable meaningfulprediction of core, penumbra and perfusion from mCTA images usingsoftware that has been trained via machine learning to interpret mCTAimages.

In a first aspect, a method of predicting any one of or a combination ofcore, penumbra and perfusion status in a stroke patient from a series ofcurrent multi-phase computed tomography (mCTA) images obtained from acurrent patient is described, the method including the steps of: withina database of historical data, the historical data having a plurality ofhistorical images from patients having undergone computed tomographyperfusion (CTP) study and non-contrast computed tomography (NCCT) andwherein the historical images have been previously analyzed to identifyhistorical features of interest including an estimate of core, penumbraand perfusion status, i. analyzing the current mCTA images andidentifying current features of interest wherein the current features ofinterest are determined by an analysis of density value, time andlocation from the current mCTA images; and, ii.comparing the currentfeatures of interest from step i against corresponding historicalfeatures of interest and fitting the current features of interest to thehistorical features of interest to predict any one of or a combinationof core, penumbra and perfusion status in the current mCTA images. ThemCTA images preferably include 3-5 phases of images.

In various embodiments, the method may also include the followingfeatures or steps:

-   The historical data includes treatment data associated with each    patient including no reperfusion treatment or reperfusion treatment    data pertaining to the historical images.-   The database includes data analysis of follow-up non-contrast CT    (NCCT) and/or diffusion weighted (DW) magnetic radiation imaging    (MRI) of the historical images describing patient outcome.-   The database includes data analysis of past mCTA studies from a    second group of patients that have undergone mCTA and no perfusion    treatment and follow-up non-contrast CT (NCCT) and/or diffusion    weighted (DW)MRI.-   The database includes data analysis of past-mCTA studies from a    second group of patients that have undergone mCTA and perfusion    treatment and follow-up non-contrast CT (NCCT) and/or diffusion    weighted (DW)MRI.-   Steps i and ii are completed in 10 minutes or less after obtaining    the current mCTA images.-   The historical data includes data quantifying a patient’s recovery    status between full recovery to poor recovery.-   The historical data includes any one of or a combination of a    calculation of time to maximum (Tmax), cerebral blood volume (CBV)    and cerebral blood flow (CBF) values from the historical images.-   The method includes the step of quantifying a likelihood of success    of conducting a reperfusion treatment based on a comparison of    predicted core, penumbra and/or perfusion status in the current    patient and actual outcome data from historical data.

In another aspect, a method of quantifying core and/or penumbra from aplurality of current multi-phase computed tomography (mCTA) images of apatient is described, the method including the steps of: introducing theplurality of mCTA images into a prediction model, the prediction modelderived from historical computed tomography perfusion (CTP) image dataand CTP study data that quantified Time to Maximum (Tmax), cerebralblood volume (CBV) and cerebral blood (CBF) from the historical CTPimage data and wherein the prediction model fits the current mCTA imagesinto the prediction model to predict core and/or penumbra from thecurrent mCTA images.

In various embodiments, the method includes may also include thefollowing features or steps:

-   The historical CTP image data further includes patient treatment    data, patient posttreatment follow-up images and patient outcome    data and where the prediction model fits a current patient    core/penumbra prediction to the patient outcome data to obtain a    prediction of outcome of the current patient.-   The patient treatment data includes surgical procedure data whether    undertaken or not.-   The method includes the steps of calculating prediction maps and    displaying the prediction maps on a display system and where the    prediction maps include core and/or penumbra as core and/or penumbra    prediction maps.-   The method includes the steps of calculating an outcome score for a    current patient based on a calculation of total core and/or penumbra    and/or diffusion, fitting the total core and/or penumbra and/or    diffusion to past patient data having outcome data and displaying    the outcome score on a display system.-   The method includes the steps of predicting core/penumbra within 10    minutes of initially obtaining current mCTA images.

In another aspect, a method of building and training a machine learningdatabase to enable prediction of any one of or a combination of core,penumbra and perfusion status from multi-phase computed tomography(mCTA) images is described, comprising the steps of: i. introducinghistorical patient data into a database, the historical patient dataincluding images from multiple computed tomography perfusion (CTP)studies and treatment follow-up images; ii. analyzing the historicalpatient data to extract features of interest relating to occlusionlocation, core, penumbra and perfusion; iii. introducing historical mCTApatient data in the database, the historical mCTA patient data includingmultiple sets of mCTA images and testing the sets of mCTA imagesobtained in step i using a machine-learning algorithm, where each set ofmCTA images include phases of images and follow-up images; iv. derivinga classifier prediction model from step iii; and, v. introducing asingle set of mCTA image data into the prediction model from step iv andanalyzing the mCTA image data to produce any one of or a combination ofa core, penumbra and status prediction probability map for the mCTAimage data.

In various embodiments, the method includes the following features orsteps:

-   The historical patient data includes data from patients having    undergone reperfusion and patients not having undergone reperfusion.-   The historical mCTA patient data includes data from patients having    undergone reperfusion and patients not having undergone reperfusion.-   The prediction model calculates predicted core volume.-   The prediction model calculates predicted penumbra volume.-   The prediction model calculates predicted tissue perfusion status.-   The prediction model determines follow-up infarct volume and    utilizes the follow-up infarct volume as a reference standard for    step v.-   The step of feature extraction includes the steps of analyzing    density and acquisition time of features.-   Density and acquisition time analysis includes for each voxel:    -   calculating average and standard deviation of Hounsfield Units        (HU) across each phase of mCTA images;    -   calculating a coefficient of variance of HUs for each phase of        mCTA images;    -   calculating slopes of HUs between any two phases of mCTA images;    -   determining peak of HUs across the phases of mCTA images; and,    -   determining a time peak of HUs.-   The features are calculated in the neighborhood centered at each    voxel at different scales.-   The method includes the step of comparing the mCTA prediction    probability map against follow-up images to test the accuracy of the    model.

In another aspect, a method is described, comprising the steps of:accessing, at one or more computing devices, a plurality of multi-phasecomputed tomography angiogram (mCTA) images from a current patient;determining using an image classification engine whether the accessedimage includes any one of or a combination of core or penumbra, whereinthe image classification engine has been trained, using unsupervisedlearning, to estimate from the mCTA images a quantity of core andpenumbra; and, displaying via a graphical user interface a graphicalrepresentation of the quantity of core and/or penumbra. The method mayinclude a step of estimating and displaying perfusion status.

In another aspect, a method of building and training a machine learningdatabase and model to enable prediction of any one of or a combinationof core, penumbra and perfusion status from sets of multi-phase computedtomography (mCTA) images and sets of computed tomography perfusion (CTP)images is described, where each set of mCTA images include phases ofimages and follow-up images, the method including the steps of: i.introducing historical patient mCTA and CTP images into a database andanalyzing the mCTA and CTP images to extract features of interestrelating to occlusion location, core, penumbra and perfusion; ii.testing multiple sets of mCTA images against patterns obtained in step iusing a machine-learning algorithm; deriving a classifier predictionmodel from step ii; iii. introducing a single set of mCTA image datainto the prediction model from step iii and analyzing the mCTA imagedata to produce any one of or a combination of a core, penumbra andperfusion status prediction probability map for the mCTA image data;and, iv. comparing the mCTA prediction probability map against follow-upimages to ascertain the accuracy of the model.

In various embodiments, the method may also include the followingfeatures or steps:

-   Steps ii and iii includes two-stage training including a first    penumbra stage that derives a penumbra area and a second core stage    that derives a core area.-   The machine learning model comprises one of a random forest, a    support vector machine, a neural network, or a k nearest neighbor    model.-   The features of interest relating to occlusion location, core,    penumbra and perfusion, are identified by any one of or a    combination of first-order statistics including mean and histogram    of HU values, and texture features including gray-level    co-occurrence matrix and gray level run length matrix.-   The features of interest relating to occlusion location, core,    penumbra and perfusion are calculated at different scales for a    given voxel corresponding to the axial imaging and where the    features of interest are calculated at low, median, and    high-resolution scales.-   The features of interest contributing to occlusion location, core,    penumbra and perfusion are automatically selected using a feature    selection module utilizing any one of or a combination of univariate    selection, feature importance, and correlation matrix with heatmap.-   The at least one probability map is thresholded to generate infarct    core and/or penumbra and/or perfusion volume for an axial imaging    slice.-   Morphological operations including dilation and/or erosion and    component analysis are applied after thresholding to remove isolated    islands.-   The model enables prediction of any one of or a combination of core    and penumbra within a multiple label machine learning model    including a core label, penumbra label and normal tissue label.-   The method includes the step of inputting historical patient meta    data including age, sex, NIHSS, ASPECTS, and occlusion site.

In another aspect, a method of predicting a plurality of contrastenhanced volumes in a brain scan image is described including the stepsof: from a series of multi-phase computed tomography (mCTA) images froma stroke patient and a plurality of historical images from patientshaving undergone non-contrast computed tomography (NCCT) and computedtomography perfusion (CTP) study, comparing signal intensityfluctuations of voxel data of the mCTA images against correspondingvoxels from the historical CTP images and time synchronizing a pluralityof mCTA volumes to a plurality of CTP volumes; from time synchronizedmCTA and historical CTP volumes, comparing corresponding voxels from themCTA images and historical CTP images and finding at least one match ofhistorical CTP images; and, utilizing the at least one match ofhistorical CTP images as basis for predicting a contrast enhanced volumefor the mCTA images. The method may include the step of building anddisplaying at least one predictive map showing a combination of core andpenumbra and/or perfusion.

In another aspect, a method of deriving and presenting informationuseful in diagnosing medium vessel occlusion (MeVO) in a current patientis described including the steps of: from a plurality of CT imagesshowing hypoperfused regions of the current patient; i. quantifying ahypoperfused tissue volume in the current patient; ii. comparing thehypoperfused tissue volume from step i to threshold volume parametersdefining a MeVO event and determining if the hypoperfused tissue matchesvolume parameters of a MeVO event; and, iii. if a MeVO event isdetermined, display a MeVO event determination.

In various embodiments, the method may also include the followingfeatures or steps:

-   Steps i and ii include quantifying a hypoperfused tissue shape in    the current patient and comparing the hypoperfused tissue shape to    threshold shape parameters defining a MeVO event and determining if    the hypoperfused tissue shape matches shape parameters of a MeVO    event.-   Steps i and ii include quantifying a hypoperfused tissue location in    the current patient and comparing the hypoperfused tissue location    to threshold location parameters defining a MeVO event and    determining if the hypoperfused tissue location matches location    parameters of a MeVO event.-   Steps i and ii include quantifying involved cortex.-   Steps i and ii include quantifying hypoperfused tissue confluence in    the current patient and comparing the hypoperfused tissue confluence    to hypoperfused tissue confluence parameters defining a MeVO event    and determining if the hypoperfused tissue confluence matches    hypoperfused tissue confluence of a MeVO event.-   The method further includes the steps of correlating the    hypoperfused tissue location to corresponding hypoperfused locations    from historical patient data wherein historical patient data    includes data marking past MeVO events; and determining a best fit    of historical patient image data and marking current patient images    with MeVO location data derived from the historical patient image    data.-   The historical patient data with past MeVO events includes data    quantifying proximal voxel location relevant to a past MeVO event    within a past patient record.-   Historical patient data records have been previously analyzed to    derive 2D and/or 3D relationships between level 1-3 vessels.-   Historical patient data records have been previously analyzed to    define volumes of tissue as level 1, level 2, or level 3 tissue and    wherein each volume of level 1, level 2 or level 3 tissue has at    least one, equal, distal or proximal relationship with an adjacent    volume of tissue.-   The method includes the step of, after step iii, examining changes    in contrast densities in adjacent proximal volumes across two or    more phases of CTA images for the current patient and based on those    changes marking changes in contrast density as normal flow or    abnormal flow.-   The method includes the step of discarding volumes showing normal    flow from further analysis.-   The method includes the step of utilizing volumes showing normal    flow as a baseline for contrast density analysis.-   The method includes the step of marking volumes showing abnormal    flow for further analysis.-   The method includes the step of analyzing zones where contrast    abruptly transitions from no contrast to significant contrast    between adjacent images or vice versa to identify vessels of    interest.-   The method includes the step of marking and displaying zones where    contrast abruptly transitions on CTA images of the current patient.-   The method includes the steps of providing at least one output    selected from any one of or a combination of: a) presence or not of    MeVO; b) zone of interest marking and c) vessel of interest.

In another aspect, a method of deriving and presenting informationuseful in diagnosing medium vessel occlusion (MeVO) in a current patientis described, comprising the steps of: from a plurality of CT imagesshowing at least one hypoperfused region of the current patient; i.identifying the at least one hypoperfused region and correlating the atleast one hypoperfused regions to one or more corresponding hypoperfusedregions from within historical patient data; and, ii. deriving andidentifying immediately proximal vessels/zones in the current patientbased on best match(s) to the historical patient data and marking theproximal vessel/zones as predicted MeVO locations on current patient CTimages.

In various embodiments, the method includes the following features orsteps:

-   The method includes the steps of quantifying a hypoperfused tissue    shape in the current patient and comparing the hypoperfused tissue    shape to threshold shape parameters defining a MeVO event and    determining if the hypoperfused tissue shape matches shape    parameters of a MeVO event.-   The method includes the steps of quantifying a hypoperfused tissue    location in the current patient and comparing the hypoperfused    tissue location to threshold location parameters defining a MeVO    event and determining if the hypoperfused tissue location matches    location parameters of a MeVO event.-   The method includes the steps of quantifying involved cortex.-   The method includes the steps of quantifying hypoperfused tissue    confluence in the current patient and comparing the hypoperfused    tissue confluence to hypoperfused tissue confluence parameters    defining a MeVO event and determining if the hypoperfused tissue    confluence matches hypoperfused tissue confluence of a MeVO event.-   The method includes the steps of correlating the hypoperfused tissue    location to corresponding hypoperfused locations from historical    patient data wherein historical patient data includes data marking    past MeVO events; determining a best fit of historical patient image    data and marking current patient images with MeVO location data    derived from the historical patient image data.-   The historical patient data with past MeVO events includes data    quantifying proximal voxel location relevant to a past MeVO event    within a past patient record.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, features and advantages of the invention will beapparent from the following description of particular embodiments of theinvention, as illustrated in the accompanying drawings. The drawings arenot necessarily to scale, emphasis instead being placed uponillustrating the principles of various embodiments of the invention.Similar reference numerals indicate similar components.

FIG. 1 is a graph showing typical time density curves and acquisition ofmCTA images.

FIG. 1A is a graph showing 3 typical mCTA data points vs. time inaccordance with one embodiment of the invention.

FIG. 2 is a representative graph showing a typical impulse residuefunction calculated from a deconvolution algorithm in CTP software inaccordance with the prior art.

FIG. 3 is a flowchart showing an overall training and testing strategyof a machine learning model to predict ischemic infarct, penumbra andperfusion status in accordance with one embodiment of the invention.

FIG. 3A is a flowchart showing a training and testing strategy ofmachine learning models to predict (a) ischemic infarct, penumbra and(b) perfusion status in accordance with one embodiment of the invention.

FIG. 4 is an illustration showing d₁, d₂ and dd patterns in accordancewith one embodiment the invention showing representative contrast delayfor different patterns.

FIG. 4A is an illustration showing d₁, d₂ and dd patterns as in FIG. 4overlaid with representatives zones for color coding in accordance withone embodiment of the invention.

FIG. 4B is a representative graph of data points from a set of mCTAimages having p0-p3 data points and showing how tissue intensity curves(TICs) from past studies have been matched to the data points.

FIG. 4C is a figure illustrating a process of machine learning inaccordance with one embodiment of the invention.

FIG. 5 is a flowchart showing details of training and testing each ofthree machine learning models.

FIG. 5A is a flowchart showing details of patient inclusion.

FIG. 6 is an overview of input images and input data including patientmeta data and input artery/vein functions together with representativeoutput data that may be displayed after modelling and prediction.

FIGS. 6A and 6B are representative mCTA and prediction maps from twocase studies.

FIG. 7 is a representative image showing anterior, middle, and posteriorarteries at levels 2 and 3 in accordance with the prior art.

FIGS. 7A-7H are a sequence of images in a patient showing a progressionof a diagnostic process using mCTA prediction maps and MeVO predictiontools together with conventional imaging. FIG. 7A shows an unremarkablenon-contrast CT scan in a patient having an acute stroke with aphasia;FIG. 7B shows the area of the brain that is hypoperfused resulting inthe patients symptoms; the map could be created using an mCTA predictionmap; FIG. 7C shows a zone of interest predicted using the MeVO tool;FIG. 7D shows identification of a particular vessel of interestfollowing further processing with the MeVO tool; FIG. 7E shows vesselocclusion from conventional angiography; FIG. 7F shows a follow-up imagefollowing successful reperfusion; FIG. 7G is a flowchart outlining aprocess of providing useful information to a physician diagnosing MeVOin accordance with one embodiment of the invention; and FIG. 7H shows arepresentative 2D and partial 3D map of the relationship of L1-L3voxels.

FIG. 8 shows multiphase CTA predicted infarct probability maps comparedto CTP (Tmax) map and follow up infarct. There are 3 examples, eachlabelled as per rows.

FIG. 8A show Bland-Altman plots of (a) mCTA penumbra volume predictedusing the proposed model vs. follow up infarct volume for 44 patientswho did not achieve acute reperfusion (mTICI 0/1); (b) mCTA infarctvolume predicted using the proposed model vs. follow up infarct volumefor 100 patients who had acute reperfusion (mTICI 2b/2c/3); and (c) mCTAperfusion volume predicted using the proposed model vs. time-dependentTmax predicted infarct volume for all 144 patients in the test cohort.

DETAILED DESCRIPTION

With reference to the figures, systems, and methods for predictingischemic brain tissue fate from multi-phase CT-angiography (mCTA) aredescribed. More specifically, systems and methods are described thatenable meaningful prediction of core, penumbra and perfusion from mCTAimages using software that has been trained via machine learning tointerpret mCTA images.

Terms used herein have definitions that are reasonably inferable fromthe drawings and description.

Introduction

Various aspects of the invention will now be described with reference tothe figures. For the purposes of illustration, components depicted inthe figures are not necessarily drawn to scale. Instead, emphasis isplaced on highlighting the various contributions of the components tothe functionality of various aspects of the invention. A number ofpossible alternative features are introduced during the course of thisdescription. It is to be understood that, according to the knowledge andjudgment of persons skilled in the art, such alternative features may besubstituted in various combinations to arrive at different embodimentsof the present invention.

A primary objective is to obtain from a relatively small number of mCTAimages (typically 3-phases of CTA images), a meaningful prediction ofcore, penumbra and perfusion using a methodology and software that hasbeen trained to interpret mCTA images.

As noted above, mCTA does not have the granularity of data that a CTPstudy provides. Hence, without additional boundaries and/or knowledge tointerpret the mCTA data, the mCTA images are, on their own, noteffective in accurately quantifying core, penumbra and perfusion.

Data Used in Building a Prediction Model

The inventors have determined that by using image data from past CTPstudies, and specifically from a first groups of patients that haveundergone:

-   a. CTP and no reperfusion treatment,-   b. CTP and reperfusion treatment, and-   c. follow-up Non-Contrast CT (NCCT)/Diffusion weighted (DW) MRI,

as well as image data from past mCTA studies from a second group ofpatients that have undergone:

-   d. mCTA and no perfusion treatment,-   e. mCTA and reperfusion treatment; and,-   f. follow-up Non-Contrast CT (NCCT)/Diffusion weighted (DW) MRI,

a database of scenarios that shows the “start to finish” outcomes forthese groups of patients can be utilized to build and test predictionmodels.

In accordance with the invention, models have been developed and trainedwith the objective of being able to interpret current mCTA images in aclinical setting to create clinically meaningful core, penumbra andperfusion maps at the time treatment decisions are being made.

For background, an ischemic stroke patient that has gone through a CTPand/or mCTA diagnosis and treatment protocol may have an outcome that isanywhere between a full recovery (no core) or poor recovery (significantcore). This same patient may have been subjected to either a reperfusiontreatment or no reperfusion treatment.

It has been determined that by studying the diagnostic and follow-upimages of a number of these patients, patterns of effects can beobserved across the population. For example, past data from a patientcohort (eg. 40 patients having an M1 occlusion) having undergone CTP andfollow-up studies, the CTP studies will have determined a range of Tmax,CBV and CBF values that enabled CTP maps to be created that showed core,penumbra and perfusion predictions for these patients. These patientswill have undergone (or not) treatments as well as follow-up imagingthat verifies an outcome. Similarly, for a different patient cohort,mCTA studies have been used to make treatment decisions. Again,treatments will have been undertaken (or not) as well as follow-upimaging that verifies an outcome.

By using this data from past patients within models and training themodels to interpret past mCTA images, it has been determined that at thetime of diagnosis and the time that treatment decisions are being madewith a current patient, these models can be utilized to fit mCTA datawithin the models to create predictive maps (like those obtained by CTP)that can be utilized by the physician to give an idea of the likelihoodof success of a treatment. For example, a decision to treat or not totreat may be made given the relative likelihood of success based on apredicted core/penumbra and/or perfusion status.

Building and Testing the Prediction Models

In this invention, mCTA images were analyzed against the boundariesdefined by the above databases using machine learning procedures. Asnoted above, mCTA images are effective in diagnosing and makingtreatment decisions; however, until now have been unable to be used astools to quantitatively predict core, penumbra and perfusion status.

Thus, the models sought to determine if information from mCTA images canbe correlated to data from CTP studies, be then used to createcore/penumbra/perfusion maps (ostensibly at the time of diagnosis andtreatment decision) and then based on follow-up images demonstrated thatthe prediction maps correlated well to the final outcome as determinedby final outcome images.

The models were built based on knowledge of the flow of contrast dyethrough affected and unaffected tissues in the cerebral arteries.

FIG. 1 shows representative curves of the flow of dye at one location(i.e., a zone of interest) through unaffected (i.e., contralateralvessels; shown as the artery input function) and affected (i.e.,ipsilateral vessels; shown as the tissue density curve). As can be seen,contrast dye will generally flow through affected and unaffected tissuesdifferently showing variations as different times to peak dye as well asdifferent slopes for the rise and fall of dye.

It is understood that for a CTP study, up to 50 sequences images wouldbe taken such that the contrast dye curves as shown in FIG. 1 would havea typical resolution as shown by the curves whereas for an mCTA study,images would only be taken at time points t_(0,)t₁-t₃ thus havingsubstantially lower resolution of contrast dye flow as compared to theCTP curves.

For example, as shown in FIG. 1A, only three data points, S1, S2 and S3representing contrast density at different times are obtained.

Returning to FIG. 1 and with reference to FIG. 2 , the difference inflow is explained as follows. As the dye enters the cerebral vessels,the flow bifurcates to the ipsilateral and contralateral vessels. Forboth CTP and mCTA studies, a series of x-ray images are taken as thebolus of dye enters these vessels. As the contrast flows steadilythrough the cerebral arteries, the x-ray images will show the relativeconcentration of dye throughout the arteries at a given time andlocation on both sides. The relative flow rate of the dye will determinehow long the dye will be seen. That is, fast flowing dye will rise inconcentration rapidly but also decrease in concentration rapidly (as perthe artery input function), whereas slower moving dye will take longerto accumulate and longer to clear (as per the tissue density curve).

Thus, images obtained at different times will show directly andindirectly, the flow of contrast through the brain arteries at thedifferent times. In unaffected vessels, contrast will appear and willhave substantially disappeared between t₀ and t₃. Further, contrast willpeak around t₁; be dropping away by t₂ and be less than about 25% of thepeak of t₁ by t₃.

For stroke affected tissues, shown as the tissue density curve, the flowof contrast will be time-delayed where for a given location, if contrastis being held up, the peak flow will be time-shifted to a later time,the peak contrast may be lower as compared to unaffected tissues and thetime to clear and rate of clearance may be different.

As shown in FIG. 2 , after the data has been obtained, for a CTP studythe data is processed by deconvolution algorithms to create impulseresidual functions and enable various parameters to be assessed.

FIG. 2 shows a number of the physiological parameters that can bederived from the contrast density curves and the impulse residualfunctions. As introduced above, these include:

-   a. Mean Transit Time (MTT) which represents the length of time in    seconds that it takes for blood to move from arteries to capillaries    to veins. MTT=CBV/CBF. An increase in MTT indicates a vasodilatory    response to reduced blood flow.-   b. Time to Maximum (Tmax) which represents the time at which the    maximum value of the residual function occurred and represents a    delayed arrival of contrast agent.-   c. Cerebral Blood Volume (CBV) is the area under the FIG. 2 curve    and is the volume flow rate through cerebral vasculature per unit    time (ml/100g of brain tissue)-   d. Cerebral Blood Flow (CBF) is the maximum value of the FIG. 2    curve and is the amount of blood flowing through capillaries per    unit time per unit tissue (ml/min/100 g of brain tissue). It can be    used to identify areas of hypoperfusion. Infarct core show decreased    CBF by <30%.

Other parameters can be derived including:

-   e. Time to Peak (TPP) which represents the time in seconds to reach    peak voxel enhancement. TPP is an indicator of delayed flow in the    setting of stenosis or occlusion and is increased when abnormal.-   f. Mismatch Volume is the difference in volume between total    hypoperfused area and core infarct and equals penumbra. Mismatch    ratio is the ratio of total hypoperfused area and core infarct.

Tmax and CBF are the main parameters used to determine core andpenumbra.

From FIGS. 1 and 1A, for a mCTA study, contrast density values areobtained from images taken at t₁, t₂ and t₃. Hence for a given location,as shown in FIG. 1 , data points a, b and c are obtained for theipsilateral side and data points d, e and f are obtained from thecontralateral side. Importantly, as minimal data is obtained (ascompared to CTP), the peak contrast density between t1 and t2 has notbeen measured and thus the peak level is not known from a directmeasurement.

Machine Learning Models- Development and Testing Overview

Testing and evaluation protocols were developed using three machinelearning models including, a core, penumbra and perfusion model,explained in detail below.

The core model seeks to predict the volume of core, the penumbra modelseeks to predict the volume of penumbra and the perfusion model seeks topredict tissue perfusion status.

FIG. 3 is an overview of the process for model development, testing, useand refinement. Images from past patients having undergone CTP,reperfusion and follow-up studies 30 a as well as images from patientshaving undergone CTP, no reperfusion and follow-up studies 30 b are eachsubjected to feature extraction analysis 30 c and density/acquisitiontime analysis 30 d. Data from these analyses is used to build and trainthe models 30 e. Data from a current patient having undergone an mCTAstudy 30 f can then be introduced into the model 30 e to outputpredictive penumbra/core/perfusion maps 30 g for clinical use. Follow upstudies on the current patient (eg. NCCT) 30 h can be subsequentlyintroduced to the model to enable model refinement.

FIG. 3A shows details/example of the process for more specificdevelopment/derivation of each of the (a) penumbra, core/infarctionprediction maps and (b) perfusion prediction maps. As shown in FIG.3A(a), images from 96 patients having undergone CTP, reperfusion, andfollow-up as well as images from 44 patients having undergone CTP, noreperfusion and follow-up were analyzed for feature extraction. TestmCTA images (including follow up images) were subjected to the sameprocess. After training with all images, using the penumbra model, themodel was then tested on the mCTA images (without the follow up images)to predict penumbra for the mCTA images. A similar strategy was thenused for infarction/core prediction.

Feature extraction involves analyzing density and acquisition time ofareas of interest, namely those areas that may be showing abnormal flowof contrast (ipsilateral side) and the corresponding features on thecontralateral side where flow is normal. That is, the steps of featureextraction will examine a baseline density level and look for changes indensity across each image. Those areas where density is showing changeabove a threshold level is marked for further analysis whereas thoseareas where density does not change above the threshold level will notbe marked and not subject to further analysis.

More specifically, zones of interest may be determined by evaluating thefollowing:

-   1) average and standard deviation of Hounsfield units (HUs) across    3-phase CTA images;-   2) coefficient of variance of HUs in 3-phase CTA images;-   3) changing slopes of HUs between any two phases;-   4) peak of HUs in 3-phase CTA images;-   5) time of peak HU.

The size of the zone of interest may be variable and/or adjusteddepending on the desired resolution. For example, features werecalculated in zones centered at each voxel at three scales (3x3x3,7x7x7, and 11x11x11 voxels) and then normalized using z-score method.

Analysis of changing slopes between images at different times providesuseful information about how quickly contrast agent may be flowing intoor out of affected tissues at the scale of individual or a definednumber of voxels obtained from the mCTA imaging information. As shown inFIG. 1A, for three phases, three signal intensity (SI) values can bedetermined (eg. SI₁ - SI₃) relative to a baseline HU (density) value.From these data points, the difference d in SI values between points,enables calculation of the slope of signal intensity change betweenphases. Slopes may be positive + or negative -. For example, d₁, d₂ anddd are defined as follows:

d₁ = Sl₂ − Sl₁

d₂ = Sl₃ − Sl₂

$\begin{array}{l}{\text{dd} = \text{d}_{\text{2}}\text{-d}_{\text{1}}} \\{\mspace{6mu}\mspace{6mu}\mspace{6mu}\mspace{6mu}\, = \,\,\,\text{Sl}_{\text{3}}\text{-Sl}_{\text{2}}\text{-}\left( {\text{Sl}_{\text{2}}\text{-Sl}_{\text{1}}} \right)} \\{\mspace{6mu}\mspace{6mu}\mspace{6mu}\,\mspace{6mu} = \text{Sl}_{\text{1}}\text{-2Sl}_{\text{2}}\text{+Sl}_{\text{3}}}\end{array}$

dd is the 2^(nd) derivative of the change of slope between the twolines. The slopes of each of d₁, d₂ and dd are calculated together withtheir sign (i.e.,+ or -) and used as a basis for understanding thecontrast delay for a particular location which can then be used toassign a tissue health value to that location.

FIG. 4 is a representation of how the slopes may vary based on therelative delay of contrast arriving at a particular location. Forexample, if there is substantially no delay of contrast arriving at theipsilateral location of interest, one would expect flow characteristicsof normal tissue. Three-point line pattern 40, shows points X, Y and Zdefining two lines having slopes d₁, d₂. As shown in FIG. 1 , X, Y and Zare similar to points d, e and f on the artery input function.Similarly, three-point line pattern 42, shows points A, B and C definingtwo lines having slopes d₁’, d₂’ which are similar to points a, b and con the tissue density curve of FIG. 1 and shows the delay. As contrastis increasingly held up, the tissue density curve as shown in FIG. 1will be time-shifted to the right; thus, producing three-point linepatterns similar to those shown in FIG. 4 .

Accordingly, depending on the time delay, each three-point line patternwill have a range of profiles as shown in FIG. 4 where the time delayincreases from the right to left direction.

The ± patterns of the calculated d₁, d₂, dd values represent differentscenarios of contrast flow as shown in Table 2 and FIG. 4A which showsthe different patterns together with representative circulation valueranging from 1 (good flow) to 5 (poor flow) which can be indicative ofrelative tissue health. Information about how contrast is flowing to alocation and out of a location can be determined from each three-pointline function.

TABLE 2 d-Slope Interpretation and Representative Tissue Health Codingon Ipsilateral Side Group Patterns d₁ d₂ dd Interpretation RelativeCirculation Value 44a - - + Voxel showing shortest delay (if any) ofcontrast reaching the location. 1 44b - - - Voxel showing next shortestdelay in contrast reaching the location. Contrast has reached the voxelquickly and is showing a slight delay in contrast washout 2 44c, d + - -Voxel showing peak contrast density at around the time the phase 2 imagewas taken. 3 44e + + - Voxel showing that contrast density is startingto peak at around the time the phase 2 image was taken. 4 44f + + +Voxel showing greatest delay. Across all phases, contrast density iscontinuing to rise. 5 ** combinations of [- + -] and [+ - +] are notmathematically plausible.

Each group pattern can be provided with threshold values to determinewhich group a particular line pattern may be categorized within.Different groups may be defined with individual, or a range of colorsused for subsequent color mapping of a particular voxel. The aboveanalysis is performed for each voxel of an image volume of interest.

To obtain meaningful prediction data, group patterns are matched to pastdata showing similar patterns.

As shown in FIG. 4B, the group pattern determined above can be matchedto past patient images showing similar group patterns. For example, FIG.4B shows 3 tissue input curves, TIC, TIC1 and TIC2 which represent thedata from 3 CTP studies and with “matching” phase data points p1-p3 andbaseline p0. As each of the CTP studies have determined penumbra, coreand perfusion maps, data from past studies provides a range of valuesfor penumbra, core and perfusion which can thus be used to provide abasis for predicting these values for the current patient and used togenerate 2- and/or 3-dimensional color-coded image/maps. That is, datafrom the past studies can be processed by appropriate deconvolutionalgorithms to generate CT perfusion maps such as Tmax, CBV and CBF foreach TIC from the past studies.

Model Development-Machine Learning

Referring back to FIGS. 3 and 3A, as noted above, mCTA images as well asNCCT at baseline are introduced into the model and matched to the bestfit within the CTP datasets to provide both an mCTA penumbra predictionand mCTA core prediction.

Hence, a prediction of Tmax, CBV and CBF for each can be estimated forthe 3-phase mCTA study based on the CT perfusion maps from the paststudies.

In one embodiment, as shown in FIG. 4C, machine learning techniques suchas deep neural network can be used to interpolate a TIC having p0-pmpoints at discrete time points to create a predicted TIC represented bycontinuous data points C0-Cn, which can then be utilized to buildpredicted CT perfusion maps.

In further detail, as shown in FIG. 5 , in one embodiment, the followingsteps were undertaken in processing the mCTA images to build the models.

Step 1-Analysis of data from mCTA images

-   a. Pre-processing mCTA training images. This includes spatially    aligning 3-phase mCTA and NCCT at baseline in order to correct    patient movement during acquisition; and standardizing density    values of mCTA by subtracting density value of NCCT from each phase    CTA at each voxel location.-   b. Analyzing the mCTA images and identifying features of interest    for each voxel location wherein the features of interest are derived    from density values across three phases and acquisition time of each    phase of the mCTA. These features of interest are extracted and    includes data from infarcted and normal tissue on the ipsilateral    side as defined by the follow-up imaging. This information was then    normalized by comparing it with the ipsilateral hemisphere via a    z-scoring method. The z-scoring method was performed by subtracting    the mean value of an image and divided by the standard deviation of    the image.

Step 2-Training

a. Training a random forest classifier using the features of interestfrom step 1b while using follow up infarct segmentation as an indicator.For example, a “1” is assigned to represent an infarct voxel whereas a“0” is assigned to represent normal tissue. This information is thenused to generate a penumbra probability map, indicating how likely avoxel in mCTA will be infarcted if no reperfusion is achieved. Randomforest classifier is an ensemble learning method for classification thatoperates by constructing a multitude of decision trees at training timeand outputting the class that is the mode of the classes(classification) of the individual trees.

Step 3-Applying a Threshold

a. Applying a threshold on the probability map from step 2 to generate abinary mask of penumbra tissue. For example, a “1” in this binary maskrepresents an infarct voxel whereas a “0” represents normal tissue.

Other Modelling Techniques

The machine learning model may be constructed using other modellingtechniques including support vector machine, neural network and/or knearest neighbor techniques.

Model Validation

After predicted core, infarction and perfusion maps are created,correlation to the “actual” outcome can be made to determine theaccuracy of the models. As discussed below in the validation study, themodels were statistically validated.

Additional System and Model Functionality

The models are effective in assisting a physician in making treatmentdecisions during diagnosis. As discussed above, for patients with acuteischemic stroke, time to treatment is well correlated to patientoutcome; hence obtaining effective information to enable a physician tomake a treatment decision as soon as possible is desired. As such, thesteps to determine and present core, penumbra and perfusion status fromthe time current mCTA images are introduced into the system are ideallycompleted in 10 minutes or less.

Additional data can also be introduced into the past patient databaseand presented as additional information to the physician. In oneembodiment, the past patient database includes information about patientoutcome following treatment or not as may be input after an NCCTfollow-up study has been completed. Thus, upon creating a prediction mapas described above for the current patient, data from one or more of theclosest match past patient studies describing patient outcome may bepresented to the physician. Such outcome data may be a quantified andstandard assessment score as known. For example, a past patient studymay include treatment information that a successful thrombectomy at aparticular region of interest was completed within 40 minutes of imagesbeing obtained and the patient made a good recovery. A second pastpatient study showing similar map information may include informationthat treatment occurred in 90 minutes and that patient recovery waspoor. As such, the physician can use this information as additionalinformation to evaluate if they should initiate a specific treatment.

In other embodiments, likelihood of success of a treatment may bepresented and be correlated to any one of or a combination of predictedcore, penumbra and/or perfusion status.

In other embodiments, features of interest and patterns relating toocclusion location, core, penumbra and perfusion, include any one of ora combination of the first-order statistics, such as mean and histogramof HU values, and texture features, such as gray-level co-occurrencematrix and gray level run length matrix.

The features of interest relating to occlusion location, core, penumbraand perfusion are calculated at different scales; for a given voxelcorresponding to the axial imaging, the features are calculated at low,median, and high-resolution scales.

In various embodiments, the features of interest mostly contributed toocclusion location, core, penumbra and perfusion are automatically ormanually selected using feature selection technique in order to improveprediction accuracy, reduce overfitting, and reduce training time. Thefeature selection technique includes univariate selection, featureimportance, and correlation matrix with heatmap.

In various embodiments, each probability map is thresholded to generateinfarct core and/or penumbra and/or perfusion volume for the axialimaging slice.

In other embodiments, morphological operations including image dilationand/or erosion and component analysis are applied after thresholding toremove isolated islands.

In another embodiment, the machine learning model enables prediction ofa combination of core and penumbra from a multiple label machinelearning model, that is, label 1 denotes core, label 2 denotes penumbra,and label 3 denotes normal tissue. The single model can predict core andpenumbra at the same time.

In other embodiments, as shown in FIG. 6 features at a patient level areselectively used as inputs, such as artery and vein input functionsmanually or automatically selected, together with meta data includingage, sex, NIHSS, ASPECTS, occlusion site.

Case Examples

FIG. 6 shows a hypothetical example, in which, artery and vein inputfunctions and meta data are used as additional features to refine thedeveloped machine learning model and better predict tissue status,especially for the patients with small or no occlusions.

FIGS. 6A and 6B are examples of application of the system to createeffective diagnostic maps. FIG. 6A shows representative mCTA images foran 83 year old female showing a left ICA occlusion with a stroke onsetto CT time of 95 mins, NIHSS 23, and ASPECTS 10.

FIG. 6B shows representative mCTA images for an 87 years old maleshowing left distal M1 occlusion with a stroke onset to CT time of 208mins, NIHSS 28, and ASPECTS 5.

Medium Vessel Occlusion (MeVO) Tool and Use

In various embodiments as illustrated in FIGS. 7-7H, the system mayadditionally be used to assist in diagnosing MeVO and provide differentoutputs to a diagnosing physician with varying levels of detail that maybe relevant to treatment decisions. As described below, levels of outputmay include:

-   a) a simple indication that MeVO (as opposed to LVO or SVO) is    likely present,-   b) identification of zones of interest where MeVO may be present;    and/or,-   c) specific identification of locations/vessels where MeVO may be    present.

Improved MeVO detection is achieved through utilization of CTA imagesand/or with prediction maps (e.g., core/penumbra/perfusion) togetherwith additional functionality within the system including anatomicalmaps built from a plurality of patients and/or knowledge obtained byprediction models/learning algorithms as described above.

For example, in one embodiment, as with the general prediction mapsystem described above, the MeVO system/tool is trained with past imagesand used to create effective prediction maps for a current patient thatcan be used to locate and quantify hypoperfused tissue and subsequentlyevaluate if the parameters of the hypoperfused tissue are indicative ofMeVO.

In accordance with one embodiment, the steps of identifying MeVO may beachieved automatically or semi-automatically by the following generalprocess:

-   a. Assess location of affected tissue from various combinations of    CTA images, CTP studies, mCTA studies as may be available.-   b. From the hypoperfused area, define a general zone of interest    where MeVO may be present.-   c. Determine additional parameters of the affected tissue including    the shape, size/volume, confluence, involvement of the cortex and    sub-cortical white matter and knowledge of the known supply by    vessels in that region.-   d. Determine the 2D/3D position, size, and shape of the hypoperfused    area. The position, size and shape are determined by assembling    voxels showing affected tissue characteristics that share a boundary    with adjacent voxels showing affected tissue in both proximal and    distal positions. The assembled voxels define a quantifiable volume    (i.e., based on a calculated number of linked voxels) and shape    characteristics (e.g., a characteristic “cone”, “frusto-conical” or    “wedge” shape in the distal direction). The location of the volume    is compared to known regions of the brain based on general knowledge    of brain anatomy or specific knowledge of brain anatomy from a    match/correlation analysis (as described below). Table 3 shows    various characteristics that can be assessed.-   e. Determine if the hypoperfused volume is LVO, MeVO or SVO based on    the analysis in step d.

TABLE 3 Characteristics of LVO, MeVO and SVO Characteristic LVO MeVO SVOVolume (ml) Range: 80-400 Common: 150-180 Range: 25-80 Common: 30-70 <25Shape Cone/Wedge Cone/Wedge Often indeterminate Confluence Yes Yes NoCortex Involved Mostly Maybe Sub-cortical white matter Involved InvolvedMaybe Possible sites of Occlusion per hemisphere 1-2 6-20 >20 (oftenbeyond resolution of CTA)

-   f. If it is determined that MeVO is likely, additional analysis can    be conducted depending on the desired outputs as described above. If    greater precision is desired a zone of interest analysis can be    conducted to highlight a zone(s) on the images where further    investigation could be conducted. A vessel of interest analysis may    also be conducted to identify a vessel(s) of interest. Generally, to    conduct these analyses:    -   i. Contrast densities from adjacent proximal voxels from one or        more phases of CTA images are searched for variations in        contrast density that may signal that contrast is flowing        normally or abnormally within one or more nearby voxels.    -   ii. Voxels that indicate normal contrast flow may be discarded        from further analysis and/or be utilized as a baseline for        determining if contrast is abnormal or normal in nearby voxels.    -   iii. Voxels indicating abnormal contrast flow may be flagged for        further analysis.    -   iv. For those voxels showing abnormal flow, additional analysis        is conducted to highlight zones/vessels where contrast abruptly        transitions from no contrast to significant contrast between        adjacent images or vice versa.    -   v. Highlighted zones and/or vessels may be automatically marked        on CTA images as a suggestion to the physician to focus        attention in a particular area.

The foregoing is illustrated by the following illustrative example. Asnoted above, FIG. 7 is a representative figure showing the structure anddistribution of level 1-3 vessels. From known anatomy, a general patternof interconnected volumes can be built through successive levels fromproximal to distal vessels as shown schematically in FIG. 7H. That is,L1 vessels as represented by individual voxels may be positioned in2D/3D space. L2 vessels are represented by voxels surrounding orbranching off the L1 vessels to a defined distance and L3 vessels arerepresented by voxels surrounding the L2 vessels to a defined distance.

Thus, from a prediction map, a hypoperfused zone may be identified andcorrelated to a 3D location (for example, a particular M2/M3 zone) andthus to a general location in the brain. With knowledge that vesselsproximal to that location are generally perfused by adjacent areas in aknown direction, corresponding proximal voxels on the images may beflagged for additional investigation.

Importantly, voxels that may be distal and/or beyond a particularthreshold distance from the hypoperfused area may be discarded fromfurther processing. Similarly, proximal voxels beyond a thresholddistance may also be discarded.

Further processing can look for a variety of changes within thoseflagged voxels, including normal and abnormal contrast flow and/orcollateral filling from a later CTA image.

In one embodiment, different phases of voxels (eg. from mCTA) areoverlaid with respect to one another to help identify a “missing vessel”i.e., one where no contrast is directly observed but contrast behaviournearby suggests its presence.

FIG. 7G describes a process of providing additional information to aphysician in accordance with one embodiment to assist in diagnosingMeVO. In this example, output to the physician is derived from acombination of current patient images 90 a and historical images 90 bwhich are utilized by a model 90 c as generally described above. Fromthe model, prediction maps (eg. core/penumbra/perfusion) 90 d areanalysed to identify hypoperfused regions 90 e in a current patienttypically marked by color coding. Analysis to determine if thehypoperfused volume is likely LVO, MeVO or SVO is conducted. If MeVO issuggested 90 e, these maps are correlated to specific 2D areas/volumesin the brain of the current patient and can then be compared andcorelated to corresponding hypoperfused areas from historical dataimages 90 f. The best fit historical data images, having associatedL1-L3 mapping data, is/are used to identify corresponding proximal zonesin the current patient images 90 g, which are marked as zones ofinterest for the physician to examine for further MeVO diagnosis.

In various embodiments, historical images may be filtered to limit thedataset to MeVO images only 90 i.

In addition, and prior to comparison with current patient images, 2D/3Drelationships between level 1-3 vessels can be derived 90 j and as shownin FIG. 7H, wherein numerous volumes of tissue are assembled into asuccessive branch structure in 2D/3D space. FIG. 7H shows a graphicalrepresentation of voxels labelled according to their level and thatbased on their level may touch voxels of the same label or one levelhigher/lower. That is, a level 1 voxel can only directlytouch/communicate with a level 2 voxel etc. Shown as a representative 2Dand partial 3D map, FIG. 7H shows two zones where a number of L3 voxelsare grey representing the color code for hypoperfused tissue as shown atone level (shown as smaller dotted ellipse). From this, the system caninitially search for voxels distal to most proximal hypoperfused levelas a starting point to calculate the location, volume and shape of thehyperperfused volume. In FIG. 7H, this is shown as the volume definedbetween the two dotted ellipses (ie. a general frustoconcial shape). Thedetection of the location of MeVO is determined by analysis of proximalvoxels that are contiguous with those L3 voxels as a likely location ofan occlusion.

For illustrative purposes only, non-confluent voxels are shown which areunlikely to be present in a typical MeVO case.

MeVO Application

In various clinical settings, the MeVO tool can be used to assist intreatment/triaging decisions. As shown in FIG. 7G, a first level output90 l may simply provide output that MeVO likely exists. If thisinformation is provided based on imaging conducted at a smaller centersome distance from a main center, the diagnosing physician may be ableto make an effective triaging decision to transfer the patient to thelarger center, where the expertise in providing reperfusion treatmentcan be provided in a reasonable time frame. In this embodiment, thedetermination of MeVO may be based on various factors including thesize, shape, location, confluence of the hypoperfused area and/orinvolvement of cortical grey matter and may include quantitative outputsregarding the likelihood of the diagnosis (e.g., 90% probability theocclusion is MeVO).

At other centers, particularly where treatment options may be available,additional outputs may be provided. These may include marking zones ofinterest as per analysis conducted at step 90 h and/or conductingfurther analysis 90 k that allows more specific identification ofvessels of interest 90 n.

Case Example

An 88-year-old female, arriving from home presented with expressiveaphasia and mild right sided weakness since 2h; NIHSS on presentation:10.

A perfusion map from CTP or a predictive perfusion map from mCTA asdescribed above was obtained indicating an area of brain washypoperfused.

The size and location characteristics of the hypoperfused area indicateda likely occluded vessel in an adjacent and proximal vessel. Based onthe volume of tissue that is hypoperfused, an estimate of the size ofvessel is made. The MeVO tool predicts and marks one or more areas wherethe occlusion is likely to be allowing the physician to quickly focusattention on those areas.

In various embodiments, past patient images are subjected to machinelearning analysis to refine the precision of locating potentialocclusion sites based on evaluations and variations across multiple pastimages.

As above, when the current patient images are introduced into the model,they are analyzed to find past patient images most correlated to thecurrent images. As a result, the accuracy of predicting the location ofthe MeVO may be improved.

As shown in FIG. 7A for the above patient, an initial non-contrast CTscan was unremarkable. Following mCTA, a prediction map as shown in FIG.7B was produced showing a specific hypoperfused region. Based on therelative position of the hypoperfused region, the MeVO tool determinesand identifies a particular zone of interest in specific scan images asshown by hashed circles in FIG. 7C.

Additional analysis (manual or automatic) is conducted within that zoneto identify an occluded vessel marked by the arrow in FIG. 7D. Dependingon the protocols followed and for the purposes of illustration,occlusion was confirmed by conventional angiogram shown in FIG. 7E.Following reperfusion, follow-up imaging shows the opened vessel asshown in FIG. 7F.

Validation Study

As described above, Multiphase CT-Angiography (mCTA) provides timevariant images of the pial vasculature supplying brain in patients withacute ischemic stroke (AIS). Described below is a machine learning (ML)technique that predicts infarct, penumbra and tissue perfusion from mCTAsource images.

Study Methodology

284 patients with acute ischemic stroke (AIS) were included. Allpatients had non-contrast CT, mCTA and CTP imaging at baseline andfollow up MRI/NCCT imaging. Of the 284 patient images, 140 patientimages were randomly selected to train and validate three ML models topredict infarct, penumbra, and perfusion parameter on CTP, respectively.The remaining unseen 144 patient images independent of the derivationcohort were used to test the derived ML models. The predicted infarct,penumbra, and perfusion volume from ML models was spatially andvolumetrically compared to manually contoured follow up infarct andtime-dependent Tmax thresholded volume (CTP volume), using Bland-Altmanplots, concordance correlation coefficient (CCC), intra-classcorrelation coefficient (ICC), and Dice similarity coefficient (DSC).

Study Results

Within the test cohort, Bland-Altman plots showed that the meandifference between the mCTA predicted infarct and follow up infarct was21.7 mL (limit of agreement (LoA): -41.0 to 84.3 mL) in the 100 patientswho had acute reperfusion (mTICI 2b/2c/3), and 3.4 mL (LoA: -66 to 72.9mL) in the 44 patients who did not achieve reperfusion (mTICI 0/1).Amongst reperfused subjects, CCC was 0.4 [95%CI: 0.15-0.55, P<0.01] andICC 0.42 [95% CI: 0.18-0.50, P<0.01]; in non-reperfused subjects CCC was0.52 [95%CI: 0.2-0.6, P<0.001] and ICC 0.6 [95% CI: 0.37-0.76, P<0.001].No difference was observed between the mCTA and CTP predicted infarctvolume for the overall test cohort (P=0.67).

Multiphase CT Angiography is able to predict infarct, penumbra andtissue perfusion, comparable to CT perfusion imaging.

Study Background

Ischemic infarct core estimated using CT perfusion (CTP) at admissionmay be used in treatment decision making for patients with acuteischemic stroke (AIS).¹⁻ ⁴ Classification of infarct core and penumbrais achieved using tissue perfusion estimates derived using adeconvolution algorithm from repeated serial imaging. The mismatch ratiobetween salvageable tissue (penumbra) volume and infarct core volume canbe used for selecting patients presenting beyond 6 hours and up to 24hours from last known well.³ CTP is limited by varying standardizationof CTP parameter thresholds across different vendors, longer acquisitiontimes and consequent susceptibility to patient motion, increasedradiation dose, limited coverage (with some scanners) and the need foradditional technical expertise to acquire the images.⁵⁻⁷

Multiphase computed tomographic angiography (mCTA) has been similarlyused to select patients with AIS for endovascular therapy (EVT) inrecent clinical trials.^(8,) ⁹ Advantages of this technique compared toCTP are simpler image acquisition, lower radiation exposure, noadditional contrast compared to single-phase CTA, and whole-braintime-resolved images of pial arteries and veins beyond an occlusionwhile also determining thrombus location, size, vessel patency andtortuosity.^(10,) ¹¹ Multiphase CTA imaging has not been as commonlyused to predict ischemic tissue fate on a voxel by voxel basis, in thesame way as CTP imaging. However, recent studies have demonstrated thatmCTA can be used to predict tissue fate regionally, similar to CTP.¹²⁻¹⁴An ability to harness the advantages of mCTA while producing brain mapsthat estimate tissue perfusion and predict tissue fate is likely to beof significant clinical utility.

The study aimed to develop a machine learning based technique toestimate infarct core, penumbra and tissue perfusion in patients withacute ischemic stroke.

Study Materials and Methods

Data from the Prove-IT study (Precise and Rapid assessment ofcollaterals using multi-phase CTA in the triage of patients with acuteischemic stroke for IA Therapy), a multicenter study that acquired acutemultimodal CT imaging including NCCT, multiphase CTA imaging (threephases), and CTP at baseline among ischemic stroke patients. ^(10,12)This study was approved by the local institutional review board.

Study Participants

Subjects who had (1) baseline non-contrast-enhanced CT (NCCT) and mCTA;(2) baseline CTP imaging with >=8 cm z-axis coverage; (3) hadreperfusion assessed on conventional angiography after thrombolysistreatment (intravenous tPA, endovascular therapy, or both) with themodified thrombolysis in cerebral infarction [mTICI]); and (4) had24/36-hour follow-up imaging on diffusion MRI or NCCT were included inthis analysis. Patient inclusion and exclusion are shown in FIG. 5A. 284patients, of whom, 196 patients had acute reperfusion (mTICI 2b/2c/3)and 88 patients did not (mTICI 0/1) were included.

Image Preprocessing

Each CTP study was processed using commercially availabledelay-insensitive deconvolution software (CT Perfusion 4D, GEHealthcare, Waukesha, WI). Absolute maps of cerebral blood flow (CBF, mL■ min⁻¹ ■ (100 g)⁻¹], cerebral blood volume (CBV, mL ■ (100 g)⁻¹], andTmax (seconds) were generated. Average maps were created by averagingthe dynamic CTP source images. Time-dependent Tmax thresholds confirmedpreviously, were used to generate baseline CTP thresholded maps(perfusion volume).^(6,7)

NCCT and mCTA images were first skull stripped.¹⁵ Three-phase CTA imageswere then aligned using rigid-body registration to account for patientmovement. The aligned 3-phase CTA images were registered onto NCCTimages using affine registration. Two radiologists (>5 years’experience) used ITK-SNAP and consensus to manually delineate theinfarct region on follow-up DWI/NCCT imaging .¹⁶ The follow-up imagesalong with manual infarct segmentations and CTP average maps wereregistered onto NCCT images, thus bringing all images into the sameimage space. When registration was sub-optimal, manual refinement of theregistered infarct segmentations was attempted. The NiftyReg tool wasused for all image registration tasks.¹⁷

Machine Learning Model

For the analysis, infarct core was defined as tissue that is infarctedon follow-up imaging even with reperfusion. Penumbra was defined asischemic tissue that was not infarct core but infarcts on follow-upimaging when reperfusion is not achieved. These definitions of infarctcore and penumbra are operational in context and not biological. Theperfusion map used was a Tmax map thresholded using previously publishedtime dependent thresholds.^(6,7)

Three machine learning models were developed: (1) Infarct model; (2)Penumbra model; and, (3) Perfusion model.

A 2-stage training mechanism was developed to train two machine learningmodels to predict infarct core and penumbra respectively. The detailedtraining and testing strategy is shown in FIG. 3A.

Of 88 patients without acute reperfusion (mTICI 0/1), 44 patients (35for training and 9 for validation) were randomly selected to derive arandom forest classifier at the first stage for prediction of follow-upinfarction in the non-reperfused patients (Penumbra model), while theremaining 44 patients with mTICI 0/1 independent of the derivationcohort were used to test this derived Penumbra Model. Of those 196patients with mTICI 2b/2c/3, 96 patient images (70 for training and 26for validation) randomly selected were first processed by the 1^(st)stage Penumbra model, generating penumbra probability maps. Theseprobability maps along with mCTA images were then used as inputs toderive the second random forest classifier at the second stage forinfarct prediction (Infarct model) using follow up infarct manuallysegmented as a reference standard, while the remaining 100 patients withmTICI 2b/2c/3 reperfusion independent of the derivation cohort were usedto test the derived Infarct Model. The final predictions are shown asinfarct core and penumbra where penumbra is defined as affected tissuefrom the penumbra model minus affected tissue from the infarct coremodel (FIG. 5 ).

In order to show the ability of mCTA to estimate tissue perfusion atbaseline compared to CTP imaging, the 140 patient images used fortraining and validating the Penumbra and Infarct models were reused totrain and validate the third random forest classifier (Perfusion model).For deriving and testing this model, time dependent Tmax thresholdedmaps were used as reference standard.^(6,) ⁷ The 144 images used fortesting Penumbra and Infarct models independent on the derivation cohortwere used to test the Perfusion model.

All three random forest models shared the same self-designed features asinputs. NCCT HU values were first subtracted from 3-phase CTA images,leading to a 3-point time intensity curve (TIC) for each voxel. Severalfeatures were extracted from the time intensity curve (TIC) for eachvoxel and used for deriving and testing the three random forestclassifiers.

These include: 1) average and standard deviation of Hounsfield units(HUs) across 3-phase CTA images; 2) coefficient of variance of HUs in3-phase CTA images; 3) changing slopes of HUs between any two phases; 4)peak of HUs in 3-phase CTA images; 5) time of peak HU.

All these features were calculated in the neighborhood centered at eachvoxel at three scales (3×3×3, 7×7×7, and 11×11×11 voxels) and thennormalized using z-score method. The hyper-parameters for each randomforest model, such as the number of trees in the forest and the maximumdepth of trees, etc., were optimized using 5-fold cross validation usingthe respective validation cohort. Specifically, in 5-foldcross-validation, all the original samples are randomly partitioned into5 equal sized subgroups. Of the 5 subgroups, a single subgroup isretained as the validation data for testing the model, and the remainingt subgroups are used as training data. The cross-validation process isthen repeated 5 times, with each of the 5 subgroups used exactly once asthe validation data. The 5 results can then be averaged to produce asingle estimation. Class weight was set to account for the imbalancedsample distribution based on the ratio of positive and negative samples.The random forest classifiers derived from the training and validationdataset was then applied to the test cohort to generate a probabilitymap for each patient. The probability map was then thresholded by afixed value of 0.35, followed by image post-processing, such as isolatedisland removal and morphological operation, to generate the mCTApredicted volume. The thresholding value was optimized and determinedfrom the validation cohort.

The fixed thresholding value of 0.35 was achieved by maximizing the Dicecoefficients between the thresholded binary mask and reference standardof follow up infarct segmentation while varying different discretethresholding values using the validation cohort. Isolated island removalwas used to discard small clustered random noise in the thresholdedbinary mask. Morphological operation includes image erosion and dilationfollowed by hole-filling in the binary mask.

Statistical Methods

Expert contoured follow up lesion volume (Follow up infarct volume) wereused as standard reference to evaluate mCTA predicted infarct core andpenumbra volume for the test cohort. Time-dependent Tmax thresholdedvolumes (CTP volume) were used as standard reference to evaluate themCTA perfusion volume for the test cohort. Bland-Altman plots were usedto illustrate mean differences and limit of agreement (LoA) between mCTApredicted and follow up infarct volume, and CTP volume. Literal andrelative volume agreement between mCTA predicted and follow up infarctvolume, and CTP volume were also assessed using concordance correlationcoefficient (CCC) and intra-class correlation coefficient (ICC),respectively. Spatial agreement between mCTA predicted volume and followup infarct volume, and CTP volume was assessed using Dice similaritycoefficient (DSC). Rank sum test was used to assess the differencebetween any non-normally distributed data. All statistical analyses wereperformed using MedCalc 17.8 (MedCalc Software, Mariakerke, Belgium) andMatlab (The MathWorks, Inc., United States). A two-sided alpha <0.05 wasconsidered as statistically significant.

Study Results Study Participants

Patient characteristics are summarized in Table 4. No differences wereobserved between the derivation and test cohorts (all P>0.05).

TABLE 4 Patient characteristics Characteristics Derivation cohort(N=140) Test cohort (N=144) P value Median age, year (IQR) 73 (62-79) 72(62-80) 0.73 Sex, n(%) male 80 (57) 77 (53) 0.56 Median baseline NIHSS(IQR) 17 (7-23) 14 (6-18) 0.12 Median baseline ASPECTS (IQR) 9 (8-10) 9(8-10) 0.15 Median onset-to-imaging time, min (IQR) 131 (94-226) 139(88-294) 0.35 Median imaging-to-reperfusion time, min (IQR) 90 (68-115)87 (64-125) 0.97 Median onset-to-reperfusion time, min (IQR) 245(172-330) 240 (181-377) 0.71 Median follow-up infarct volume, mL (IQR)22.2(10.3-59.4) 25.9 (10.1-60.6) 0.60 Site of occlusion, n(%) ICA 22(16)26(18) 0.76 MCA:M1 73(52) 70(48) 0.64 other 45(32) 48(33) 0.63 IQR,interquartile range; NIHSS, National Institutes of Health Stroke Scale.*p<0.05.

Accuracy of mCTA in Predicting Follow Up Infarct

FIG. 8 shows three examples of the mCTA prediction maps compared to CTPTmax maps and follow up infarct.

FIG. 8A(a) illustrates a Bland-Altman agreement plot between mCTApredicted infarct core + penumbra volume and follow up infarct volumefor the 44 patients who did not receive acute reperfusion (mTICI 0/1) inthe test cohort. The mean difference between this mCTA predicted infarctcore + penumbra volume (median, 33.2; IQR, 20.6-53.2 mL) and follow upinfarct volume (median, 26.8; IQR, 12.3-54.8 mL) was 3.4 mL (LoA:-66-72.9 mL, P=0.69). The CCC between the two volumes was 0.52 [95%CI:0.2-0.6, P<0.001] while the ICC was 0.6 [95% CI: 0.37-0.76, P<0.001].The median DSC between the mCTA predicted lesion and follow up infarctwas 26.5% (IQR, 12.9-39.3%).

FIG. 8A(b) illustrates a Bland-Altman agreement between the mCTApredicted infarct volume and follow up volume for 100 patients whoachieved acute reperfusion (eTICI 2b/2c/3) in the test cohort. The meandifference between the mCTA infarct volume (median, 37; IQR, 23-58 mL)and follow up volume (median, 26; IQR, 13-54 mL) was 21.7 mL (LoA:-41.0-84.3mL, P=0.48), CCC was 0.4 [95%CI: 0.15-0.55, P<0.01] and ICC0.42 [95% CI: 0.18-0.50, P<0.01]. The median DSC between the mCTAinfarct and follow up infarct was 24.7% (IQR, 13.8-30.4%).

The association between infarct volume predicted by the mCTA infarct andPenumbra models and follow up infarct volume in the whole test cohort isshown in Table 5.

Accuracy of mCTA Predicting Perfusion Status

FIG. 8A(c) illustrates a Bland-Altman agreement between the mCTApredicted perfusion volume and CTP volume for 144 patients in the entiretest cohort. The mean difference between the mCTA perfusion (median,40.5; IQR, 22.9-59 mL) and CTP volume (median, 26.9; IQR, 6.7-56.7 mL)was 4.6 mL (LoA: -53-62.1 mL, P=0.56), CCC was 0.63 [95%CI: 0.53-0.71.P<0.01] and ICC was 0.68 [95% CI: 0.58-0.78, P< 0.001]. The median DSCbetween mCTA predicted perfusion and CTP volume was 40.5% (IQR,25.7-52.7%).

The association between the volume predicted by mCTA perfusion model andfollow up infarct volume, and between the time dependent Tmaxthresholded predicted infarct volume and follow up infarct volume in thewhole test cohort is shown in Table 5.

TABLE 5 Comparisons between infarct volumes predicted by the derivedmCTA models and CTP vs. follow up infarct volume (median, 24.8; IQR,10.5-58.8 mL) in the test cohort (n=144) mCTA Infarct and Penumbra modelmCTA Perfusion model Time dependent Tmax thresholded model (CTP) P valuePredicted volume (median [IQR], mL) 37.3[21.3,57.8] 40.5 [22.9,63] 38.3[15.0, 65.5] 0.67 Volume difference# (mean [LoA], mL) 21.7[-44, 86.3]20.4[-51.3,92.1] 22.3 [-42.6, 87.2] 0.45 DSC (median [IQR], %)22.5[13.8,30.4] 21.7[10.9,31.2] 23.2 [13.9,33] 0.55 CCC [95% CI]0.43[0.18-0.58] 0.41 [0.16-0.62] 0.45 [0.32-0.54] N/A ICC [CI] 0.5[0.29-0.64] 0.47 [0.3-0.56] 0.54 [0.3-0.64] N/A IQR, interquartilerange; LoA, limit of agreement; CI, confidence interval; DSC: Dicesimilarity coefficient between the predicted volume and follow upinfarct volume; CCC: concordance correlation coefficient; ICC:intra-class correlation coefficient. N/A: Not applicable. # Volumedifference is defined as follow up infarct volume minus modelprediction, generated from Bland-Altman analysis

Study Discussion

Multiphase CT angiography (mCTA) is a quick and easy-to-use imaging toolin selecting patients with acute ischemic stroke (AIS) forrevascularization therapy.¹⁰ The developed machine learning techniquedescribed in this study shows that tissue status can be automaticallypredicted from the mCTA just as it is currently done using CT perfusionimaging. These results demonstrate that mCTA using the methods proposedhere has similar ability to CTP imaging in predicting tissue fate.

As such, the methodologies described herein can help physicians makeclinical decisions regarding acute stroke treatment, especially inhospitals without CTP capabilities.

When comparing mCTA predicted infarct volume with follow up infarctvolume in patients who achieved acute reperfusion (mTICI 2b/2c/3), themean volume difference of 21.7 mL, CCC of 0.4, and ICC of 0.42 are fair.The mCTA predicted infarct volume agrees better with follow up infarctvolume in patients who did not achieve acute reperfusion (mTICI 0/1/2)with a mean volume difference of 3.4 mL, CCC of 0.52, and ICC of 0.6.DSCs between mCTA predicted infarct and penumbra and follow up volumeare relatively low (less than 30%). However, accurate spatialquantification of infarction in patients with AIS is complicated andlikely influenced by many pathophysiological factors, such as collateralstatus, tissue tolerance to ischemia/hypoxia, cerebral autoregulation,leukoaraiosis, fluctuation in blood pressure, hyperglycemia and time toreperfusion.^(6,) ^(7, 18) All these factors likely lead to discrepancybetween infarct volume predicted at baseline and follow up imaging.

Of note, a recent paper from the HERMES group that used validated CTPsoftware (i.e. RAPID, iSchemaView, Menlo Park, CA) showed similar DSC(median, 0.24; IQR, 0.15-0.37) between CTP predicted infarct volume andfollow up infarct volume.¹⁹ When comparing mCTA predicted perfusion mapswith CTP time dependent Tmax thresholded maps, the results show strongeragreement between the two measurements with a mean volume difference of4.6 mL, CCC of 0.63, and ICC of 0.68. The median DSC of 40.5% betweenthe mCTA predicted perfusion and CTP volume was also reasonable,suggesting good spatial overlap.

Imaging paradigms currently used in selecting patients with AIS fortreatment include non-contrast CT, single-phase CTA, or CTP. CTP,however, requires additional radiation and contrast and specificacquisition protocols that are different from NCCT and CTA. CTP issensitive to patient motion, a feature that invalidates that tool inalmost 10 to 25% of patients.²⁰ Eleven patients were excluded from thisstudy as CTP maps generated by the software were corrupted due to theexcessive patient motion during acquisition (FIG. 5A). Multiphase CTA ispotentially less prone to patient motion while being capable ofpredicting perfusion maps appropriately (Supplements: FIG. 5A). CTP alsoadds additional costs to the health system that include cost of the scanand resources allocated to training personnel to acquire the scan.^(21,)²² Multiphase CTA has minimal additional radiation, no additionalcontrast material, whole-brain coverage, relative insensitivity topatient motion and minimal additional cost to acquire over a singlephase CTA.^(10,) ^(12,) ¹³ The technique described here automates mCTAinterpretation and provides physicians with tissue prediction as well asperfusion maps, just as CTP imaging does, thus potentially increasingtheir confidence in decision making (Table 5). FIG. 8 shows an exampleof penumbra and infarct core (and mismatch) predicted using mCTA. Ofnote, physiological definitions of infarct and penumbra are differentfrom the operational definitions used in this study. Unlike conventionalmCTA but similar to CTP, the technique described in the study is capableof detecting smaller perfusion lesions in the entire brain including theposterior circulation (Supplements: FIG. 8 ).

A strength of the developed machine learning technique is that it doesnot rely on deconvolution algorithms, which plays an essential role incurrent CTP processing. Although deconvolution methods can appropriatelymodel perfusion status, the introduction of physiological variations inarterial delivery of contrast, the effects of collateral flow, andvenous outflow components of cerebral perfusion, greatly increase thecomputational complexity.^(23,) ²⁴ The number of variables and thealgorithms used to calculate these variables results in variability ingenerating CTP threshold values for estimating infarct and penumbraacross different vendor software. Additionally, numerical solutions todeconvolution greatly relies on accurate selection of artery inputfunction (AIF), a parameter that is case dependent and sensitive tonoise, especially given the low signal to noise ratio of perfusionimages, even when preprocessing, such as motion correction, temporal andspatial smoothing, and deconvolution regularization are applied.^(25,)²⁶ The deconvolution free approach developed in this study can be easilyintegrated into any imaging paradigm using NCCT and mCTA as apost-processing step, potentially obviating the need for CTP.

In conclusion, infarct core, penumbra, and perfusion status can beautomatically predicted from multiphase CTA imaging using machinelearning. This technique shows comparable accuracy to CT perfusionimaging in measuring tissue status in patients with acute ischemicstroke. This work has the potential of assisting physicians in makingtreatment decisions in clinical settings.

Although the present invention has been described and illustrated withrespect to preferred embodiments and preferred uses thereof, it is notto be so limited since modifications and changes can be made thereinwhich are within the full, intended scope of the invention as understoodby those skilled in the art.

1. A method of predicting any one of or a combination of core, penumbraand perfusion status in a stroke patient from a series of currentmulti-phase computed tomography (mCTA) images obtained from a currentpatient, the method comprising the steps of: within a database ofhistorical data, the historical data having a plurality of historicalimages from patients having undergone computed tomography perfusion(CTP) study and non-contrast computed tomography (NCCT) and wherein thehistorical images have been previously analyzed to identify historicalfeatures of interest including an estimate of core, penumbra andperfusion status, i. analyzing the current mCTA images and identifyingcurrent features of interest wherein the current features of interestare determined by an analysis of density value, time and location fromthe current mCTA images; ii. comparing the current features of interestfrom step i against corresponding historical features of interest andfitting the current features of interest to the historical features ofinterest to predict any one of or a combination of core, penumbra andperfusion status in the current mCTA images.
 2. The method as in claim 1wherein the mCTA images include 3-5 phases of images.
 3. The method asin claim 2 wherein the historical data includes treatment dataassociated with each patient including no reperfusion treatment orreperfusion treatment data pertaining to the historical images.
 4. Themethod as in claim 3 wherein the database further includes data analysisof follow-up non-contrast CT (NCCT) and/or diffusion weighted (DW)magnetic radiation imaging (MRI) of the historical images describingpatient outcome.
 5. The method as in claim 4 wherein the databasefurther includes data analysis of past mCTA studies from a second groupof patients that have undergone mCTA and no perfusion treatment andfollow-up non-contrast CT (NCCT) and/or diffusion weighted (DW) MRI. 6.The method as in claim 5 wherein the database further includes dataanalysis of past-mCTA studies from a second group of patients that haveundergone mCTA and perfusion treatment and follow-up non-contrast CT(NCCT) and/or diffusion weighted (DW) MRI.
 7. The method as in claim 1wherein steps i and ii are completed in 10 minutes or less afterobtaining the current mCTA images.
 8. The method as in claim 1 whereinthe historical data includes data quantifying a patient’s recoverystatus between full recovery to poor recovery.
 9. The method as in claim1 wherein the historical data includes any one of or a combination of acalculation of time to maximum (Tmax), cerebral blood volume (CBV) andcerebral blood flow (CBF) values from the historical images.
 10. Themethod as in claim 1 further comprising the step of quantifying alikelihood of success of conducting a reperfusion treatment based on acomparison of predicted core, penumbra and/or perfusion status in thecurrent patient and actual outcome data from historical data.
 11. Amethod of quantifying core and/or penumbra from a plurality of currentmulti-phase computed tomography (mCTA) images of a patient comprisingthe steps of: i. introducing the plurality of mCTA images into aprediction model, the prediction model derived from historical computedtomography perfusion (CTP) image data and CTP study data that quantifiedTime to Maximum (Tmax), cerebral blood volume (CBV) and cerebral bloodflow (CBF) from the historical CTP image data and wherein the predictionmodel fits the current mCTA images into the prediction model to predictcore and/or penumbra from the current mCTA images.
 12. The method as inclaim 11 where the historical CTP image data further comprises patienttreatment data, patient post-treatment follow-up images and patientoutcome data and where the prediction model fits a current patientcore/penumbra prediction to the patient outcome data to obtain aprediction of outcome of the current patient.
 13. The method as in claim12 where the patient treatment data includes surgical procedure datawhether undertaken or not.
 14. The method as in claim 11 furthercomprising the steps of calculating prediction maps and displaying theprediction maps on a display system and where the prediction mapsinclude core and/or penumbra as core and/or penumbra prediction maps.15. The method as in claim 11 further comprising the steps ofcalculating an outcome score for a current patient based on acalculation of total core and/or penumbra and/or diffusion, fitting thetotal core and/or penumbra and/or diffusion to past patient data havingoutcome data and displaying the outcome score on a display system. 16.The method as in claim 11 further comprising the step of predictingcore/penumbra within 10 minutes of initially obtaining current mCTAimages.
 17. A method of building and training a machine learningdatabase to enable prediction of any one of or a combination of core,penumbra and perfusion status from multi-phase computed tomography(mCTA) images comprising the steps of: i. introducing historical patientdata into a database, the historical patient data including images frommultiple computed tomography perfusion (CTP) studies and treatmentfollow-up images; ii. analyzing the historical patient data to extractfeatures of interest relating to occlusion location, core, penumbra andperfusion; iii. introducing historical mCTA patient data in thedatabase, the historical mCTA patient data including multiple sets ofmCTA images and testing the sets of mCTA images obtained in step i usinga machine-learning algorithm, where each set of mCTA images includephases of images and follow-up images; iv. deriving a classifierprediction model from step iii; and, v. introducing a single set of mCTAimage data into the prediction model from step iv and analyzing the mCTAimage data to produce any one of or a combination of a core, penumbraand status prediction probability map for the mCTA image data.
 18. Themethod as in claim 17 where the historical patient data includes datafrom patients having undergone reperfusion and patients not havingundergone reperfusion.
 19. The method as in claim 18 where thehistorical mCTA patient data includes data from patients havingundergone reperfusion and patients not having undergone reperfusion. 20.The method as in claim 17 where the prediction model calculatespredicted core volume.
 21. The method as in claim 17 where theprediction model calculates predicted penumbra volume.
 22. The method asin claim 17 where the prediction model calculates predicted tissueperfusion status.
 23. The method as in claim 17 where the predictionmodel determines follow-up infarct volume and utilizes the follow-upinfarct volume as a reference standard for step v.
 24. The method as inclaim 17 where the step of feature extraction includes the steps ofanalyzing density and acquisition time of features.
 25. The method as inclaim 24 where density and acquisition time analysis includes for eachvoxel: i. calculating average and standard deviation of Hounsfield Units(HU) across each phase of mCTA images; ii. calculating a coefficient ofvariance of HUs for each phase of mCTA images; iii. calculating slopesof HUs between any two phases of mCTA images; iv. determining peak ofHUs across the phases of mCTA images; and, v. determining a time peak ofHUs.
 26. The method as is in claim 25 where features are calculated inthe neighborhood centered at each voxel at different scales.
 27. Themethod as in claim 25 further comprising the step of comparing the mCTAprediction probability map against follow-up images to test the accuracyof the model.
 28. A method comprising the steps of: i. accessing, at oneor more computing devices, a plurality of multi-phase computedtomography angiogram (mCTA) images from a current patient; ii.determining using an image classification engine whether the accessedimage includes any one of or a combination of core or penumbra, whereinthe image classification engine has been trained, using unsupervisedlearning, to estimate from the mCTA images a quantity of core andpenumbra; and, iii. displaying via a graphical user interface agraphical representation of the quantity of core and/or penumbra. 29.The method as in claim 28 further comprising the step of estimating anddisplaying perfusion status.
 30. A method of building and training amachine learning database and model to enable prediction of any one ofor a combination of core, penumbra and perfusion status from sets ofmulti-phase computed tomography (mCTA) images and sets of computedtomography perfusion (CTP) images, where each set of mCTA images includephases of images and follow-up images, the method comprising the stepsof: i. introducing historical patient mCTA and CTP images into adatabase and analyzing the mCTA and CTP images to extract features ofinterest relating to occlusion location, core, penumbra and perfusion;ii. testing multiple sets of mCTA images against patterns obtained instep i using a machine-learning algorithm; iii. deriving a classifierprediction model from step ii; iv. introducing a single set of mCTAimage data into the prediction model from step iii and analyzing themCTA image data to produce any one of or a combination of a core,penumbra and perfusion status prediction probability map for the mCTAimage data; and, v. comparing the mCTA prediction probability mapagainst follow-up images to ascertain the accuracy of the model.
 31. Themethod as in claim 30 wherein steps ii and iii includes two-stagetraining including a first penumbra stage that derives a penumbra areaand a second core stage that derives a core area.
 32. The method as inclaim 30 wherein the machine learning model comprises one of a randomforest, a support vector machine, a neural network, or a k nearestneighbor model.
 33. The method as in claim 30 wherein the features ofinterest relating to occlusion location, core, penumbra and perfusion,are identified by any one of or a combination of first-order statisticsincluding mean and histogram of HU values, and texture featuresincluding gray-level co-occurrence matrix and gray level run lengthmatrix.
 34. The method as in claim 30 wherein the features of interestrelating to occlusion location, core, penumbra and perfusion arecalculated at different scales for a given voxel corresponding to theaxial imaging and where the features of interest are calculated at low,median, and high resolution scales.
 35. The method as in claim 30wherein the features of interest contributing to occlusion location,core, penumbra and perfusion are automatically selected using a featureselection module utilizing any one of or a combination of univariateselection, feature importance, and correlation matrix with heatmap. 36.The method as in claim 30 wherein at least one probability map isthresholded to generate infarct core and/or penumbra and/or perfusionvolume for an axial imaging slice.
 37. The method as in claim 36 whereinmorphological operations including dilation and/or erosion and componentanalysis are applied after thresholding to remove isolated islands. 38.The method as in claim 30 wherein the model enables prediction of anyone of or a combination of core and penumbra within a multiple labelmachine learning model including a core label, penumbra label and normaltissue label.
 39. The method as in claim 30 further comprising the stepof inputting historical patient meta data including age, sex, NIHSS,ASPECTS, and occlusion site.
 40. A method of predicting a plurality ofcontrast enhanced volumes in a brain scan image comprising the steps of:from a series of multi-phase computed tomography (mCTA) images from astroke patient and a plurality of historical images from patients havingundergone non-contrast computed tomography (NCCT) and computedtomography perfusion (CTP) study, i. comparing signal intensityfluctuations of voxel data of the mCTA images against correspondingvoxels from the historical CTP images and time synchronizing a pluralityof mCTA volumes to a plurality of CTP volumes; ii. from timesynchronized mCTA and historical CTP volumes, comparing correspondingvoxels from the mCTA images and historical CTP images and finding atleast one match of historical CTP images; and, iii. utilizing the atleast one match of historical CTP images as basis for predicting acontrast enhanced volume for the mCTA images.
 41. The method as in claim40 further comprising the step of building and displaying at least onepredictive maps showing a combination of core and penumbra and/orperfusion. 42-65. (canceled)